This study compares the magnitude of the losses that the COVID-19 pandemic inflicted across three critical dimensions: loss of life, loss of income, and loss of learning. The wellbeing consequences of excess mortality are expressed in years of life lost while those of income losses and school closures are expressed in additional years spent in poverty (as measured by national poverty lines), either currently or in the future. While the 2020-2021 period witnessed a global drop in life expectancy and the largest one-year increase in global poverty in many decades, widespread school closure may cause an increase in future poverty almost twice as large. The estimates of wellbeing loss for the average global citizen include a loss of almost 3 weeks of life (19 days), an additional two and half weeks spent in poverty in the years 2020 and 2021 (17 days), and the possibility of an additional month of life in poverty in the future due to school closures (31 days). Wellbeing losses are also not equitably distributed across countries. The typical high-income country suffered more total years of life lost than additional years in poverty, while the opposite holds for the typical low- or middle-income country. Aggregating total losses requires a valuation for a year of life lost vis-à-vis an additional year spent in poverty. If a year of life lost is valued at six or fewer additional years spent in poverty, low-income countries suffered greater total wellbeing loss than high-income countries. However, for a wide range of valuations, the greatest wellbeing losses fell on upper-middle-income countries and for countries in the Latin America region. This set of countries suffered the largest mortality costs as well as large losses in learning and sharp increases in poverty.
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Reproducible Research Repository (World Bank) | https://reproducibility.worldbank.org |
The code was run in a computer with the following specifications:
Runtime: 40 minutes
All datasets used are public and included in the reproducibility package except for the Welfare Vectors dataset. Replicators can contact the dataset authors (specified in the datasets section) for access.
Author | Affiliation | |
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Benoit Decerf | World Bank | bdecerf@worldbank.org |
Jed Friedman | World Bank | jfriedman@worldbank.org |
Arthur Mendes | World Bank | agalegomendes@worldbank.org |
Steven Pennings | World Bank | spennings@worldbank.org |
Nishant Yonzan | World Bank | nyonzan@worldbank.org |
2024-02
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World | WLD |
The materials in the reproducibility packages are distributed as they were prepared by the staff of the International Bank for Reconstruction and Development/The World Bank. The findings, interpretations, and conclusions expressed in this event do not necessarily reflect the views of the World Bank, the Executive Directors of the World Bank, or the governments they represent. The World Bank does not guarantee the accuracy of the materials included in the reproducibility package.
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Modified BSD3 | https://opensource.org/license/bsd-3-clause/ |
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Benoit Decerf | World Bank | bdecerf@worldbank.org |
Reproducibility WB | World Bank | reproducibility@worldbank.org |
Name | Abbreviation | Affiliation | Role |
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Reproducibility WBG | DIME | World Bank - Development Impact Department | Verification and preparation of metadata |
2024-02-26
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